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4618371da5
This is a new version of #15648 based on the latest master branch. Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR. In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.) Fixes https://github.com/pytorch/pytorch/issues/71105 @ezyang Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797 Approved by: https://github.com/ezyang
69 lines
3.0 KiB
Python
69 lines
3.0 KiB
Python
import torch
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from torch.distributions import constraints
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from torch.distributions.exponential import Exponential
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from torch.distributions.transformed_distribution import TransformedDistribution
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from torch.distributions.transforms import AffineTransform, PowerTransform
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from torch.distributions.utils import broadcast_all
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from torch.distributions.gumbel import euler_constant
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__all__ = ['Weibull']
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class Weibull(TransformedDistribution):
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r"""
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Samples from a two-parameter Weibull distribution.
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Example:
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>>> # xdoctest: +IGNORE_WANT("non-deterinistic")
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>>> m = Weibull(torch.tensor([1.0]), torch.tensor([1.0]))
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>>> m.sample() # sample from a Weibull distribution with scale=1, concentration=1
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tensor([ 0.4784])
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Args:
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scale (float or Tensor): Scale parameter of distribution (lambda).
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concentration (float or Tensor): Concentration parameter of distribution (k/shape).
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"""
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arg_constraints = {'scale': constraints.positive, 'concentration': constraints.positive}
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support = constraints.positive
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def __init__(self, scale, concentration, validate_args=None):
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self.scale, self.concentration = broadcast_all(scale, concentration)
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self.concentration_reciprocal = self.concentration.reciprocal()
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base_dist = Exponential(torch.ones_like(self.scale), validate_args=validate_args)
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transforms = [PowerTransform(exponent=self.concentration_reciprocal),
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AffineTransform(loc=0, scale=self.scale)]
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super(Weibull, self).__init__(base_dist,
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transforms,
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validate_args=validate_args)
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def expand(self, batch_shape, _instance=None):
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new = self._get_checked_instance(Weibull, _instance)
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new.scale = self.scale.expand(batch_shape)
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new.concentration = self.concentration.expand(batch_shape)
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new.concentration_reciprocal = new.concentration.reciprocal()
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base_dist = self.base_dist.expand(batch_shape)
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transforms = [PowerTransform(exponent=new.concentration_reciprocal),
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AffineTransform(loc=0, scale=new.scale)]
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super(Weibull, new).__init__(base_dist,
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transforms,
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validate_args=False)
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new._validate_args = self._validate_args
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return new
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@property
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def mean(self):
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return self.scale * torch.exp(torch.lgamma(1 + self.concentration_reciprocal))
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@property
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def mode(self):
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return self.scale * ((self.concentration - 1) / self.concentration) ** self.concentration.reciprocal()
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@property
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def variance(self):
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return self.scale.pow(2) * (torch.exp(torch.lgamma(1 + 2 * self.concentration_reciprocal)) -
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torch.exp(2 * torch.lgamma(1 + self.concentration_reciprocal)))
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def entropy(self):
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return euler_constant * (1 - self.concentration_reciprocal) + \
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torch.log(self.scale * self.concentration_reciprocal) + 1
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